# coding:utf-8
from __future__ import print_function
# from gen_captcha import gen_captcha_text_and_image
from digit_old import gen_old
import string
import numpy as np
import tensorflow as tf
import os,shutil
from PIL import Image

# text, image = gen_captcha_text_and_image()
text, image = gen_old()
print("验证码图像channel:", image.shape,text)  # (60, 160, 3)
# 图像大小
IMAGE_HEIGHT = 60
IMAGE_WIDTH = 160
MAX_CAPTCHA = len(text)
print("验证码文本最长字符数", MAX_CAPTCHA)   # 验证码最长4字符; 我全部固定为4,可以不固定. 如果验证码长度小于4，用'_'补齐

char_set = string.digits  # 字符集，如果验证码长度小于4, '_'用来补齐

# char_set = string.digits + string.ascii_letters + '_'  # 如果验证码长度小于4, '_'用来补齐
CHAR_SET_LEN = len(char_set)

# 把 彩色图像数组 转为 灰度图像数组（色彩对识别验证码没有什么用）
def convert2gray(img):
  if len(img.shape) > 2:
    gray = np.mean(img, -1)
    # 上面的转法较快，正规转法如下
    # r, g, b = img[:,:,0], img[:,:,1], img[:,:,2]
    # gray = 0.2989 * r + 0.5870 * g + 0.1140 * b
    return gray
  else:
    return img

"""
cnn在图像大小是2的倍数时性能最高, 如果你用的图像大小不是2的倍数，可以在图像边缘补无用像素。
np.pad(image,((2,3),(2,2)), 'constant', constant_values=(255,))  # 在图像上补2行，下补3行，左补2行，右补2行
"""

# 文本转向量 (1,4*63)
def text2vec(text):
  text_len = len(text)
  if text_len > MAX_CAPTCHA:
    raise ValueError('验证码最长4个字符')

  vector = np.zeros(MAX_CAPTCHA*CHAR_SET_LEN)
  def char2pos(c):
    if c =='_':
      k = 62
      return k
    k = ord(c)-48
    if k > 9:
      k = ord(c) - 55
      if k > 35:
        k = ord(c) - 61
        if k > 61:
          raise ValueError('No Map')
    return k
  for i, c in enumerate(text):
    idx = i * CHAR_SET_LEN + char2pos(c)
    vector[idx] = 1
  return vector

# 向量转回文本
def vec2text(vec):
  char_pos = vec.nonzero()[0]
  text=[]
  for i, c in enumerate(char_pos):
    char_at_pos = i #c/63
    char_idx = c % CHAR_SET_LEN
    if char_idx < 10:
      char_code = char_idx + ord('0')
    elif char_idx <36:
      char_code = char_idx - 10 + ord('A')
    elif char_idx < 62:
      char_code = char_idx-  36 + ord('a')
    elif char_idx == 62:
      char_code = ord('_')
    else:
      raise ValueError('error')
    text.append(chr(char_code))
  return "".join(text)

"""
#向量（大小MAX_CAPTCHA*CHAR_SET_LEN）用0,1编码 每63个编码一个字符，这样顺利有，字符也有
vec = text2vec("F5Sd")
text = vec2text(vec)
print(text)  # F5Sd
vec = text2vec("SFd5")
text = vec2text(vec)
print(text)  # SFd5
"""

# 生成一个训练batch
def get_next_batch(batch_size=128):
  batch_x = np.zeros([batch_size, IMAGE_HEIGHT*IMAGE_WIDTH])
  batch_y = np.zeros([batch_size, MAX_CAPTCHA*CHAR_SET_LEN])

  for i in range(batch_size):
    text, image = gen_old()
    image = convert2gray(image)
    # print(image.shape)

    batch_x[i,:] = image.flatten() / 255 # (image.flatten()-128)/128  mean为0
    batch_y[i,:] = text2vec(text)

  return batch_x, batch_y

####################################################################

X = tf.placeholder(tf.float32, [None, IMAGE_HEIGHT*IMAGE_WIDTH])
Y = tf.placeholder(tf.float32, [None, MAX_CAPTCHA*CHAR_SET_LEN])
keep_prob = tf.placeholder(tf.float32) # dropout

# 定义CNN
def crack_captcha_cnn(w_alpha=0.01, b_alpha=0.1):
  x = tf.reshape(X, shape=[-1, IMAGE_HEIGHT, IMAGE_WIDTH, 1])

  #w_c1_alpha = np.sqrt(2.0/(IMAGE_HEIGHT*IMAGE_WIDTH)) #
  #w_c2_alpha = np.sqrt(2.0/(3*3*32))
  #w_c3_alpha = np.sqrt(2.0/(3*3*64))
  #w_d1_alpha = np.sqrt(2.0/(8*32*64))
  #out_alpha = np.sqrt(2.0/1024)

  # 3 conv layer
  w_c1 = tf.Variable(w_alpha*tf.random_normal([3, 3, 1, 32]))
  b_c1 = tf.Variable(b_alpha*tf.random_normal([32]))
  conv1 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(x, w_c1, strides=[1, 1, 1, 1], padding='SAME'), b_c1))
  conv1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
  conv1 = tf.nn.dropout(conv1, keep_prob)

  w_c2 = tf.Variable(w_alpha*tf.random_normal([3, 3, 32, 64]))
  b_c2 = tf.Variable(b_alpha*tf.random_normal([64]))
  conv2 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv1, w_c2, strides=[1, 1, 1, 1], padding='SAME'), b_c2))
  conv2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
  conv2 = tf.nn.dropout(conv2, keep_prob)

  w_c3 = tf.Variable(w_alpha*tf.random_normal([3, 3, 64, 64]))
  b_c3 = tf.Variable(b_alpha*tf.random_normal([64]))
  conv3 = tf.nn.relu(tf.nn.bias_add(tf.nn.conv2d(conv2, w_c3, strides=[1, 1, 1, 1], padding='SAME'), b_c3))
  conv3 = tf.nn.max_pool(conv3, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
  conv3 = tf.nn.dropout(conv3, keep_prob)

  # Fully connected layer
  w_d = tf.Variable(w_alpha*tf.random_normal([8*20*64, 1024]))
  b_d = tf.Variable(b_alpha*tf.random_normal([1024]))
  dense = tf.reshape(conv3, [-1, w_d.get_shape().as_list()[0]])
  dense = tf.nn.relu(tf.add(tf.matmul(dense, w_d), b_d))
  dense = tf.nn.dropout(dense, keep_prob)

  w_out = tf.Variable(w_alpha*tf.random_normal([1024, MAX_CAPTCHA*CHAR_SET_LEN]))
  b_out = tf.Variable(b_alpha*tf.random_normal([MAX_CAPTCHA*CHAR_SET_LEN]))
  out = tf.add(tf.matmul(dense, w_out), b_out)
  #out = tf.nn.softmax(out)
  return out

# 训练
def train_crack_captcha_cnn():
  output = crack_captcha_cnn()
  # loss
  #loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(output, Y))
  loss = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=output, labels=Y))
  # 最后一层用来分类的softmax和sigmoid有什么不同？
  # optimizer 为了加快训练 learning_rate应该开始大，然后慢慢衰
  optimizer = tf.train.AdamOptimizer(learning_rate=0.001).minimize(loss)

  predict = tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN])
  max_idx_p = tf.argmax(predict, 2)
  max_idx_l = tf.argmax(tf.reshape(Y, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)
  correct_pred = tf.equal(max_idx_p, max_idx_l)
  accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))

  saver = tf.train.Saver()
  with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())

    step = 0
    count = 0
    while True:
      batch_x, batch_y = get_next_batch(64)
      _, loss_ = sess.run([optimizer, loss], feed_dict={X: batch_x, Y: batch_y, keep_prob: 0.75})
      print('\r',step, 'loss:',loss_,end='')

      # 每100 step计算一次准确率
      if step % 10 == 0:
        batch_x_test, batch_y_test = get_next_batch(100)
        acc = sess.run(accuracy, feed_dict={X: batch_x_test, Y: batch_y_test, keep_prob: 1.})
        print('\nstep:',step,'acc:', acc)
        # 如果准确率大于50%,保存模型,完成训练
        if acc > 0.10:
          count += 1
          if count > 1:
            saver.save(sess, "crack_capcha.model", global_step=step)
            break
      step += 1

# prediction
def crack_captcha(captcha_image):
  # 此处可预测，但不能循环调用
  output = crack_captcha_cnn()

  saver = tf.train.Saver()
  with tf.Session() as sess:
    print(tf.train.latest_checkpoint('.'))
    saver.restore(sess, tf.train.latest_checkpoint('.'))

    predict = tf.argmax(tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)
    text_list = sess.run(predict, feed_dict={X: [captcha_image], keep_prob: 1})

    text = text_list[0].tolist()
    vector = np.zeros(MAX_CAPTCHA*CHAR_SET_LEN)
    i = 0
    for n in text:
      vector[i*CHAR_SET_LEN + n] = 1
      i += 1
    return vec2text(vector)

def crack_captcha_my(error_path,right_path,num):
  output = crack_captcha_cnn()

  saver = tf.train.Saver()
  with tf.Session() as sess:
    saver.restore(sess, tf.train.latest_checkpoint('.'))

    predict = tf.argmax(tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)

    count = 0
    for j in range(num):
      text_r, image_r = gen_old()
      image = convert2gray(image_r)
      image = image.flatten() / 255

      text_list = sess.run(predict, feed_dict={X: [image], keep_prob: 1})

      text = text_list[0].tolist()
      vector = np.zeros(MAX_CAPTCHA*CHAR_SET_LEN)
      i = 0
      for n in text:
        vector[i*CHAR_SET_LEN + n] = 1
        i += 1
      predict_text = vec2text(vector)

      print("\r正确: {}  预测: {}   进度:{}{}".format(text_r, predict_text,float(j+1)/num,j),end='')
    #   target = Image.fromarray(image_r,mode='RGB')
    #   if text_r != predict_text:
    #     count += 1
    #     target.save(error_path+text_r+ '_'+predict_text+'.jpg')
    #   else:
    #     target.save(right_path+text_r+'.jpg')
    # print('\nerror rate is:',float(count)/num)

if __name__ == '__main__':
  import time
  # # 模型训练
  # train_crack_captcha_cnn()

  # text, image = gen_captcha_text_and_image()
  test_error_path = '/Users/gengyanpeng/Desktop/error/'
  test_right_path = '/Users/gengyanpeng/Desktop/right/'

  # test_error_path = '/home/czc/Documents/gengyp/czc-dl/error'
  # test_right_path = '/home/czc/Documents/gengyp/czc-dl/right'

  # if os.path.exists(test_right_path):shutil.rmtree(test_right_path)
  # if os.path.exists(test_error_path):shutil.rmtree(test_error_path)
  # os.mkdir(test_right_path)
  # os.mkdir(test_error_path)

  crack_captcha_my(test_error_path,test_right_path,1000)

  # start = time.time()
  # text, image = gen_old()
  # image = convert2gray(image)
  # image = image.flatten() / 255
  # predict_text = crack_captcha(image)
  # print("正确: {}  预测: {}".format(text, predict_text),time.time()-start)

  # # 根据指定文件夹图片测试
  # import glob
  # pic_dir = '/home/czc/Desktop/num-4-czc/train'
  # pic_dir = '/Users/gengyanpeng/Desktop/gen_digit'

  # output = crack_captcha_cnn()  # net output

  # saver = tf.train.Saver()
  # with tf.Session() as sess:
  #   saver.restore(sess, tf.train.latest_checkpoint('.'))

  #   predict = tf.argmax(tf.reshape(output, [-1, MAX_CAPTCHA, CHAR_SET_LEN]), 2)


  #   for pic in glob.glob(pic_dir+'/*.*'):
  #     img = Image.open(pic).resize((160,60))
  #     # img.show()

  #     text_r = os.path.basename(pic).split('.')[0]

  #     image = convert2gray(np.array(img))
  #     image = image.flatten() / 255

  #     text_list = sess.run(predict, feed_dict={X: [image], keep_prob: 1})

  #     text = text_list[0].tolist()
  #     vector = np.zeros(MAX_CAPTCHA*CHAR_SET_LEN)
  #     i = 0
  #     for n in text:
  #       vector[i*CHAR_SET_LEN + n] = 1
  #       i += 1
  #     predict_text = vec2text(vector)

  #     print("\r正确: {}  预测: {} ".format(text_r, predict_text),end='')

  #     if text_r != predict_text:
  #       # count += 1
  #       img.save(test_error_path+text_r+ '_'+predict_text+'.jpg')
  #     else:
  #       img.save(test_right_path+text_r+'.jpg')



